1. Field of Art
The disclosure generally relates to processes that collect information about entities, assess and predict the performance of entities, including public companies, non-profits, social enterprises, early and late stage ‘startups,’ and entire ecosystems.
2. Description of the Related Art
Over $67 billion in cash was invested in startups in 2007 in the United States alone. Currently, there are no reliable quantitative tools that can systematically and analytically inform these investment decisions. The development of such tools is difficult for three reasons: the inexistence of an exhaustive database with information about these entities, the lack of standardized methods to systematically evaluate any given startup's performance, and the lack of predictive algorithms and models to forecast the future performance of entities. Currently, there is no central repository of information that gathers facts about an entity's access to financial, social and human capital, related press and reviews, as well as the social dynamics of its founders and contributors. Moreover, tracking tools that systematically compute and monitor the performance of these entities over time are lacking. Therefore, determining the factors that affect the performance of entities empirically and analytically has until now been challenging. Without these factors and inputs, it has not been possible to develop predictive algorithms.
The aforementioned reasons explain why presently there are no predictive tools to quantitatively assess the risks and potential returns of any given entity, especially those that are private (e.g., startups). Similarly, there is a lack of a universal standardized scoring system that allows the systematic comparison of startups based on the same criteria. As a consequence, it is difficult to develop a normalized ranking for startup type entities. Currently, investors manually compare and perform risk-analyses on these startups based on limited data. The volume of information (i.e., the number of data points and the depth of data about each entity) accessible by any single individual or organization was not statistically relevant. Moreover, a single individual or entity did not have access to large datasets containing information corresponding to startups across different locations and industries in the world.
Current attempts at solving the problem of effectively gathering information for analysis are resource intensive, sporadic, and unsuccessful at reaching significant coverage. Tools such as conventional due diligence and decision-making guidelines are drawn from a limited number of sample points by humans. Therefore, this human bias contributes to the current existence of investment criteria that are wrongly focused on factors that are not empirically and statistically proven to influence the future performance of an entity such as an early-stage startup. An objective and universal analysis of the performance of entities, in general, is lacking.